by Manan Shah, on Apr 20, 2022 4:24:01 PM
Estimated reading time: 3 mins
Key takeaways from the blog- Intelligent Automation simplifies the overall process architecture.
- It drives systemic resilience through end-to-end automation.
- The AI/ML-enabled automation institutionalizes continuous system evolution.
Intelligent Automation is the more sophisticated form of the automation continuum. It delivers end-to-end automation in different functions and multi-variate processes. Yet, it simplifies the process architecture and the overall automation approach. Due to these traits, Intelligent Automation is touted as an important tool in the Business Process Automation and Business Process Management technology stacks.
The goal of Intelligent Automation
During unplanned and widespread socio-economic disruptions, improving end-to-end process automation, sustainability, and resilience is as equally critical as improving productivity and efficiency. In such situations, automation, process simplification, scalable adoption, unification of siloes, straight-through processing, higher accuracy, and integration of enterprise frameworks assume a higher meaning and importance. These business asks in toto also mean higher productivity and efficiency. Nevertheless, Intelligent Automation drives these critical business requirements in almost all the process-intensive industry sectors, and CoE led championing of Intelligent Automation is the way forward.
An Intelligent Automation strategy at the helm is a must
A strategy to drive the long-term business goals is imperative. Depending on this strategizing, the CoE shortlists the technologies for the Intelligent Automation mashup to achieve significant results and envisage the bigger picture. Having key process indicators or KPIs to monitor and measure the deployment and results is a must; the paradigm goes, “what is monitored is better managed”.
Intelligent Automation usually mashes up technologies, such as intelligent document processing, robotic process automation, API connectors, workflows, low code-no code platforms, process mining, task mining, artificial intelligence/machine learning, and analytics towards building a self-learning, intelligent, and robust outcome. Intelligent document processing brings unstructured data and written text within the purview of automation. Whereas robotic process automation and APIs build end-to-end automation and process encryption towards a resilient process architecture. The workflows integrate the enterprise frameworks and simplify the processes. The AI/ML algorithms build a continuous-learning-by-exception mechanism. Here, the CoE approach performs an orchestrator role and manages the outcome through the use of analytics.
The functional aspect of Intelligent Automation
Intelligent Automation is rightly touted as a BPM technology. It involves definite aspects of business process management and business process automation that achieve a higher target through a CoE-led approach. These aspects are –
- Process Modelling: Mapping and designing the business workflow along critical paths allow building robust, end-to-end process automation and function automation models to achieve seamless transformation.
- Performance Monitoring: Analytics-enabled dashboards allow continuous monitoring towards process improvement. The monitoring of the AI/ML-enabled self-evolving mashup across various project KPIs facilitates data-driven decision-making.
- Service-oriented Architecture (SOA): Intelligent Automation is SOA based. The end-goal of systemic resilience and end-to-end automation involving legacy machines decides the technologies that go in delivering the business outcomes.
The advantages of a CoE led Intelligent Automation
A CoE-led automation effort offers an opportunity to select the right tools and platforms to deliver the desired end-results, at scale, across the enterprise. It allows monitoring across all nodes at specified time intervals and ensures continuous evolution of the Intelligent Automation platform. The dashboard-monitoring and insights-led approach allows taking data-driven decisions at key milestones. The simplification achieved through end-to-end automation provides an edge even in a disrupted market and allows looking beyond productivity and efficiency improvements.
In short
Intelligent Automation delivers to Service-Oriented Architecture. It simplifies the processes, builds systemic resilience as well as brings unstructured data within the ambit of automation. The AI/ML-enabled continuous learning ensures systemic evolution. The CoE-led approach creates measurable KPIs and performs an orchestrator role in the entire exercise.
